This project focuses on analyzing healthcare data to uncover meaningful insights, identify patterns, and build predictive models for healthcare-related outcomes.
The notebook walks through data exploration, preprocessing, visualization, statistical analysis, and machine learning modeling to demonstrate how data science can improve decision-making in the healthcare sector.
Several machine learning models were applied to the dataset, including:
- Logistic Regression
- Decision Trees
- Random Forest Classifier
- Gradient Boosting / XGBoost
The models were evaluated on classification metrics such as Accuracy, Precision, Recall, F1-score, and ROC-AUC to determine their effectiveness in predicting healthcare outcomes.
The dataset used for this analysis contains healthcare-related features such as patient demographics, medical conditions, and treatment outcomes.
Data preprocessing steps included:
- Handling missing values
- Encoding categorical variables
- Scaling numerical features
- Train/test splitting for model validation
- Environment: Python (Jupyter Notebook)
- Libraries:
pandas,numpy,matplotlib,seaborn,scikit-learn,xgboost - Results:
- High-performing models such as Random Forest and XGBoost achieved strong predictive accuracy.
- Evaluation demonstrated balanced precision and recall, making them effective for healthcare predictions.
Visualizations such as histograms, correlation heatmaps, and boxplots were used to understand data distributions and relationships between features.
Plots of training vs. validation accuracy, confusion matrices, and ROC curves illustrate how well the models performed in classifying healthcare outcomes.
(You can include sample figures from your notebook here for GitHub presentation.)
This project highlights the use of data science and machine learning in healthcare analytics.
The analysis demonstrated that ensemble models such as Random Forest and XGBoost are effective in predicting healthcare outcomes, paving the way for better patient care and decision-making.
- Incorporate larger and more diverse healthcare datasets.
- Apply advanced deep learning techniques for more complex prediction tasks.
- Develop a dashboard or web app to make predictions accessible to medical practitioners.
This project is licensed under the MIT License.